R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(7.1,0,6.8,0,6.5,0,6.3,0,6.1,0,6.1,0,6.3,0,6.3,0,6.0,0,6.2,0,6.4,0,6.8,0,7.5,0,7.5,0,7.6,0,7.6,0,7.4,0,7.3,0,7.1,0,6.9,0,6.8,0,7.5,0,7.6,0,7.8,0,8.0,0,8.1,0,8.2,0,8.3,0,8.2,0,8.0,0,7.9,0,7.6,0,7.6,0,8.2,0,8.3,0,8.4,0,8.4,0,8.4,0,8.6,0,8.9,0,8.8,0,8.3,0,7.5,0,7.2,0,7.5,0,8.8,0,9.3,0,9.3,0,8.7,1,8.2,1,8.3,1,8.5,1,8.6,1,8.6,1,8.2,1,8.1,1,8.0,1,8.6,1,8.7,1,8.8,1,8.5,1,8.4,1,8.5,1,8.7,1,8.7,1,8.6,1,8.5,1,8.3,1,8.1,1,8.2,1,8.1,1,8.1,1,7.9,1,7.9,1,7.9,1,8.0,1,8.0,1,7.9,1,8.0,1,7.7,1,7.2,1,7.5,1,7.3,1,7.0,1,7.0,1,7.0,1,7.2,1,7.3,1,7.1,1,6.8,1,6.6,1,6.2,1,6.2,1,6.8,1,6.9,1,6.8,1),dim=c(2,96),dimnames=list(c('w','d'),1:96))
> y <- array(NA,dim=c(2,96),dimnames=list(c('w','d'),1:96))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
w d M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.1 0 1 0 0 0 0 0 0 0 0 0 0 1
2 6.8 0 0 1 0 0 0 0 0 0 0 0 0 2
3 6.5 0 0 0 1 0 0 0 0 0 0 0 0 3
4 6.3 0 0 0 0 1 0 0 0 0 0 0 0 4
5 6.1 0 0 0 0 0 1 0 0 0 0 0 0 5
6 6.1 0 0 0 0 0 0 1 0 0 0 0 0 6
7 6.3 0 0 0 0 0 0 0 1 0 0 0 0 7
8 6.3 0 0 0 0 0 0 0 0 1 0 0 0 8
9 6.0 0 0 0 0 0 0 0 0 0 1 0 0 9
10 6.2 0 0 0 0 0 0 0 0 0 0 1 0 10
11 6.4 0 0 0 0 0 0 0 0 0 0 0 1 11
12 6.8 0 0 0 0 0 0 0 0 0 0 0 0 12
13 7.5 0 1 0 0 0 0 0 0 0 0 0 0 13
14 7.5 0 0 1 0 0 0 0 0 0 0 0 0 14
15 7.6 0 0 0 1 0 0 0 0 0 0 0 0 15
16 7.6 0 0 0 0 1 0 0 0 0 0 0 0 16
17 7.4 0 0 0 0 0 1 0 0 0 0 0 0 17
18 7.3 0 0 0 0 0 0 1 0 0 0 0 0 18
19 7.1 0 0 0 0 0 0 0 1 0 0 0 0 19
20 6.9 0 0 0 0 0 0 0 0 1 0 0 0 20
21 6.8 0 0 0 0 0 0 0 0 0 1 0 0 21
22 7.5 0 0 0 0 0 0 0 0 0 0 1 0 22
23 7.6 0 0 0 0 0 0 0 0 0 0 0 1 23
24 7.8 0 0 0 0 0 0 0 0 0 0 0 0 24
25 8.0 0 1 0 0 0 0 0 0 0 0 0 0 25
26 8.1 0 0 1 0 0 0 0 0 0 0 0 0 26
27 8.2 0 0 0 1 0 0 0 0 0 0 0 0 27
28 8.3 0 0 0 0 1 0 0 0 0 0 0 0 28
29 8.2 0 0 0 0 0 1 0 0 0 0 0 0 29
30 8.0 0 0 0 0 0 0 1 0 0 0 0 0 30
31 7.9 0 0 0 0 0 0 0 1 0 0 0 0 31
32 7.6 0 0 0 0 0 0 0 0 1 0 0 0 32
33 7.6 0 0 0 0 0 0 0 0 0 1 0 0 33
34 8.2 0 0 0 0 0 0 0 0 0 0 1 0 34
35 8.3 0 0 0 0 0 0 0 0 0 0 0 1 35
36 8.4 0 0 0 0 0 0 0 0 0 0 0 0 36
37 8.4 0 1 0 0 0 0 0 0 0 0 0 0 37
38 8.4 0 0 1 0 0 0 0 0 0 0 0 0 38
39 8.6 0 0 0 1 0 0 0 0 0 0 0 0 39
40 8.9 0 0 0 0 1 0 0 0 0 0 0 0 40
41 8.8 0 0 0 0 0 1 0 0 0 0 0 0 41
42 8.3 0 0 0 0 0 0 1 0 0 0 0 0 42
43 7.5 0 0 0 0 0 0 0 1 0 0 0 0 43
44 7.2 0 0 0 0 0 0 0 0 1 0 0 0 44
45 7.5 0 0 0 0 0 0 0 0 0 1 0 0 45
46 8.8 0 0 0 0 0 0 0 0 0 0 1 0 46
47 9.3 0 0 0 0 0 0 0 0 0 0 0 1 47
48 9.3 0 0 0 0 0 0 0 0 0 0 0 0 48
49 8.7 1 1 0 0 0 0 0 0 0 0 0 0 49
50 8.2 1 0 1 0 0 0 0 0 0 0 0 0 50
51 8.3 1 0 0 1 0 0 0 0 0 0 0 0 51
52 8.5 1 0 0 0 1 0 0 0 0 0 0 0 52
53 8.6 1 0 0 0 0 1 0 0 0 0 0 0 53
54 8.6 1 0 0 0 0 0 1 0 0 0 0 0 54
55 8.2 1 0 0 0 0 0 0 1 0 0 0 0 55
56 8.1 1 0 0 0 0 0 0 0 1 0 0 0 56
57 8.0 1 0 0 0 0 0 0 0 0 1 0 0 57
58 8.6 1 0 0 0 0 0 0 0 0 0 1 0 58
59 8.7 1 0 0 0 0 0 0 0 0 0 0 1 59
60 8.8 1 0 0 0 0 0 0 0 0 0 0 0 60
61 8.5 1 1 0 0 0 0 0 0 0 0 0 0 61
62 8.4 1 0 1 0 0 0 0 0 0 0 0 0 62
63 8.5 1 0 0 1 0 0 0 0 0 0 0 0 63
64 8.7 1 0 0 0 1 0 0 0 0 0 0 0 64
65 8.7 1 0 0 0 0 1 0 0 0 0 0 0 65
66 8.6 1 0 0 0 0 0 1 0 0 0 0 0 66
67 8.5 1 0 0 0 0 0 0 1 0 0 0 0 67
68 8.3 1 0 0 0 0 0 0 0 1 0 0 0 68
69 8.1 1 0 0 0 0 0 0 0 0 1 0 0 69
70 8.2 1 0 0 0 0 0 0 0 0 0 1 0 70
71 8.1 1 0 0 0 0 0 0 0 0 0 0 1 71
72 8.1 1 0 0 0 0 0 0 0 0 0 0 0 72
73 7.9 1 1 0 0 0 0 0 0 0 0 0 0 73
74 7.9 1 0 1 0 0 0 0 0 0 0 0 0 74
75 7.9 1 0 0 1 0 0 0 0 0 0 0 0 75
76 8.0 1 0 0 0 1 0 0 0 0 0 0 0 76
77 8.0 1 0 0 0 0 1 0 0 0 0 0 0 77
78 7.9 1 0 0 0 0 0 1 0 0 0 0 0 78
79 8.0 1 0 0 0 0 0 0 1 0 0 0 0 79
80 7.7 1 0 0 0 0 0 0 0 1 0 0 0 80
81 7.2 1 0 0 0 0 0 0 0 0 1 0 0 81
82 7.5 1 0 0 0 0 0 0 0 0 0 1 0 82
83 7.3 1 0 0 0 0 0 0 0 0 0 0 1 83
84 7.0 1 0 0 0 0 0 0 0 0 0 0 0 84
85 7.0 1 1 0 0 0 0 0 0 0 0 0 0 85
86 7.0 1 0 1 0 0 0 0 0 0 0 0 0 86
87 7.2 1 0 0 1 0 0 0 0 0 0 0 0 87
88 7.3 1 0 0 0 1 0 0 0 0 0 0 0 88
89 7.1 1 0 0 0 0 1 0 0 0 0 0 0 89
90 6.8 1 0 0 0 0 0 1 0 0 0 0 0 90
91 6.6 1 0 0 0 0 0 0 1 0 0 0 0 91
92 6.2 1 0 0 0 0 0 0 0 1 0 0 0 92
93 6.2 1 0 0 0 0 0 0 0 0 1 0 0 93
94 6.8 1 0 0 0 0 0 0 0 0 0 1 0 94
95 6.9 1 0 0 0 0 0 0 0 0 0 0 1 95
96 6.8 1 0 0 0 0 0 0 0 0 0 0 0 96
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) d M1 M2 M3 M4
7.595833 0.037083 0.065590 -0.039236 0.018438 0.113611
M5 M6 M7 M8 M9 M10
0.021285 -0.146042 -0.338368 -0.568194 -0.685521 -0.140347
M11 t
-0.045174 0.004826
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.5413 -0.7078 0.1040 0.5929 1.5225
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.595833 0.355588 21.361 <2e-16 ***
d 0.037083 0.343531 0.108 0.9143
M1 0.065590 0.416309 0.158 0.8752
M2 -0.039236 0.415323 -0.094 0.9250
M3 0.018438 0.414430 0.044 0.9646
M4 0.113611 0.413628 0.275 0.7843
M5 0.021285 0.412920 0.052 0.9590
M6 -0.146042 0.412305 -0.354 0.7241
M7 -0.338368 0.411784 -0.822 0.4136
M8 -0.568194 0.411358 -1.381 0.1709
M9 -0.685521 0.411026 -1.668 0.0992 .
M10 -0.140347 0.410788 -0.342 0.7335
M11 -0.045174 0.410646 -0.110 0.9127
t 0.004826 0.006247 0.773 0.4420
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8212 on 82 degrees of freedom
Multiple R-squared: 0.1207, Adjusted R-squared: -0.01869
F-statistic: 0.8659 on 13 and 82 DF, p-value: 0.5909
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.176077488 0.352154977 0.82392251
[2,] 0.095504551 0.191009102 0.90449545
[3,] 0.051283283 0.102566566 0.94871672
[4,] 0.037257740 0.074515480 0.96274226
[5,] 0.021588588 0.043177176 0.97841141
[6,] 0.019877335 0.039754670 0.98012266
[7,] 0.016078878 0.032157755 0.98392112
[8,] 0.010712817 0.021425633 0.98928718
[9,] 0.036688761 0.073377522 0.96331124
[10,] 0.030289823 0.060579646 0.96971018
[11,] 0.018890861 0.037781722 0.98110914
[12,] 0.012340281 0.024680562 0.98765972
[13,] 0.009327052 0.018654104 0.99067295
[14,] 0.006520065 0.013040131 0.99347993
[15,] 0.004269550 0.008539100 0.99573045
[16,] 0.003869634 0.007739268 0.99613037
[17,] 0.002635864 0.005271727 0.99736414
[18,] 0.001934445 0.003868889 0.99806556
[19,] 0.001437249 0.002874499 0.99856275
[20,] 0.001070547 0.002141094 0.99892945
[21,] 0.006749711 0.013499422 0.99325029
[22,] 0.012522855 0.025045711 0.98747714
[23,] 0.010175312 0.020350624 0.98982469
[24,] 0.006220960 0.012441920 0.99377904
[25,] 0.003737482 0.007474965 0.99626252
[26,] 0.003026096 0.006052191 0.99697390
[27,] 0.046587334 0.093174669 0.95341267
[28,] 0.346178955 0.692357910 0.65382104
[29,] 0.596171706 0.807656588 0.40382829
[30,] 0.584361132 0.831277735 0.41563887
[31,] 0.583143124 0.833713751 0.41685688
[32,] 0.533323304 0.933353392 0.46667670
[33,] 0.474139182 0.948278364 0.52586082
[34,] 0.540719422 0.918561155 0.45928058
[35,] 0.614293511 0.771412978 0.38570649
[36,] 0.688476897 0.623046206 0.31152310
[37,] 0.737590534 0.524818931 0.26240947
[38,] 0.765878122 0.468243755 0.23412188
[39,] 0.897913483 0.204173034 0.10208652
[40,] 0.957739183 0.084521634 0.04226082
[41,] 0.983550925 0.032898151 0.01644908
[42,] 0.987108368 0.025783264 0.01289163
[43,] 0.985366715 0.029266570 0.01463329
[44,] 0.977343357 0.045313286 0.02265664
[45,] 0.981376203 0.037247595 0.01862380
[46,] 0.984277472 0.031445056 0.01572253
[47,] 0.985112005 0.029775989 0.01488799
[48,] 0.981004207 0.037991586 0.01899579
[49,] 0.971499258 0.057001484 0.02850074
[50,] 0.955614866 0.088770268 0.04438513
[51,] 0.930951303 0.138097393 0.06904870
[52,] 0.899312770 0.201374460 0.10068723
[53,] 0.867401237 0.265197526 0.13259876
[54,] 0.861680506 0.276638988 0.13831949
[55,] 0.883515274 0.232969451 0.11648473
[56,] 0.884014072 0.231971856 0.11598593
[57,] 0.899014938 0.201970124 0.10098506
[58,] 0.883724088 0.232551823 0.11627591
[59,] 0.859900536 0.280198928 0.14009946
[60,] 0.818574368 0.362851264 0.18142563
[61,] 0.738680271 0.522639458 0.26131973
[62,] 0.637522398 0.724955204 0.36247760
[63,] 0.593633559 0.812732881 0.40636644
> postscript(file="/var/www/html/rcomp/tmp/1mk6o1227785842.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/23vlm1227785842.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/33umy1227785842.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/44dq91227785842.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5l3gm1227785842.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 96
Frequency = 1
1 2 3 4 5 6
-0.56625000 -0.76625000 -1.12875000 -1.42875000 -1.54125000 -1.37875000
7 8 9 10 11 12
-0.99125000 -0.76625000 -0.95375000 -1.30375000 -1.20375000 -0.85375000
13 14 15 16 17 18
-0.22416667 -0.12416667 -0.08666667 -0.18666667 -0.29916667 -0.23666667
19 20 21 22 23 24
-0.24916667 -0.22416667 -0.21166667 -0.06166667 -0.06166667 0.08833333
25 26 27 28 29 30
0.21791667 0.41791667 0.45541667 0.45541667 0.44291667 0.40541667
31 32 33 34 35 36
0.49291667 0.41791667 0.53041667 0.58041667 0.58041667 0.63041667
37 38 39 40 41 42
0.56000000 0.66000000 0.79750000 0.99750000 0.98500000 0.64750000
43 44 45 46 47 48
0.03500000 -0.04000000 0.37250000 1.12250000 1.52250000 1.47250000
49 50 51 52 53 54
0.76500000 0.36500000 0.40250000 0.50250000 0.69000000 0.85250000
55 56 57 58 59 60
0.64000000 0.76500000 0.77750000 0.82750000 0.82750000 0.87750000
61 62 63 64 65 66
0.50708333 0.50708333 0.54458333 0.64458333 0.73208333 0.79458333
67 68 69 70 71 72
0.88208333 0.90708333 0.81958333 0.36958333 0.16958333 0.11958333
73 74 75 76 77 78
-0.15083333 -0.05083333 -0.11333333 -0.11333333 -0.02583333 0.03666667
79 80 81 82 83 84
0.32416667 0.24916667 -0.13833333 -0.38833333 -0.68833333 -1.03833333
85 86 87 88 89 90
-1.10875000 -1.00875000 -0.87125000 -0.87125000 -0.98375000 -1.12125000
91 92 93 94 95 96
-1.13375000 -1.30875000 -1.19625000 -1.14625000 -1.14625000 -1.29625000
> postscript(file="/var/www/html/rcomp/tmp/6gaec1227785842.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 96
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.56625000 NA
1 -0.76625000 -0.56625000
2 -1.12875000 -0.76625000
3 -1.42875000 -1.12875000
4 -1.54125000 -1.42875000
5 -1.37875000 -1.54125000
6 -0.99125000 -1.37875000
7 -0.76625000 -0.99125000
8 -0.95375000 -0.76625000
9 -1.30375000 -0.95375000
10 -1.20375000 -1.30375000
11 -0.85375000 -1.20375000
12 -0.22416667 -0.85375000
13 -0.12416667 -0.22416667
14 -0.08666667 -0.12416667
15 -0.18666667 -0.08666667
16 -0.29916667 -0.18666667
17 -0.23666667 -0.29916667
18 -0.24916667 -0.23666667
19 -0.22416667 -0.24916667
20 -0.21166667 -0.22416667
21 -0.06166667 -0.21166667
22 -0.06166667 -0.06166667
23 0.08833333 -0.06166667
24 0.21791667 0.08833333
25 0.41791667 0.21791667
26 0.45541667 0.41791667
27 0.45541667 0.45541667
28 0.44291667 0.45541667
29 0.40541667 0.44291667
30 0.49291667 0.40541667
31 0.41791667 0.49291667
32 0.53041667 0.41791667
33 0.58041667 0.53041667
34 0.58041667 0.58041667
35 0.63041667 0.58041667
36 0.56000000 0.63041667
37 0.66000000 0.56000000
38 0.79750000 0.66000000
39 0.99750000 0.79750000
40 0.98500000 0.99750000
41 0.64750000 0.98500000
42 0.03500000 0.64750000
43 -0.04000000 0.03500000
44 0.37250000 -0.04000000
45 1.12250000 0.37250000
46 1.52250000 1.12250000
47 1.47250000 1.52250000
48 0.76500000 1.47250000
49 0.36500000 0.76500000
50 0.40250000 0.36500000
51 0.50250000 0.40250000
52 0.69000000 0.50250000
53 0.85250000 0.69000000
54 0.64000000 0.85250000
55 0.76500000 0.64000000
56 0.77750000 0.76500000
57 0.82750000 0.77750000
58 0.82750000 0.82750000
59 0.87750000 0.82750000
60 0.50708333 0.87750000
61 0.50708333 0.50708333
62 0.54458333 0.50708333
63 0.64458333 0.54458333
64 0.73208333 0.64458333
65 0.79458333 0.73208333
66 0.88208333 0.79458333
67 0.90708333 0.88208333
68 0.81958333 0.90708333
69 0.36958333 0.81958333
70 0.16958333 0.36958333
71 0.11958333 0.16958333
72 -0.15083333 0.11958333
73 -0.05083333 -0.15083333
74 -0.11333333 -0.05083333
75 -0.11333333 -0.11333333
76 -0.02583333 -0.11333333
77 0.03666667 -0.02583333
78 0.32416667 0.03666667
79 0.24916667 0.32416667
80 -0.13833333 0.24916667
81 -0.38833333 -0.13833333
82 -0.68833333 -0.38833333
83 -1.03833333 -0.68833333
84 -1.10875000 -1.03833333
85 -1.00875000 -1.10875000
86 -0.87125000 -1.00875000
87 -0.87125000 -0.87125000
88 -0.98375000 -0.87125000
89 -1.12125000 -0.98375000
90 -1.13375000 -1.12125000
91 -1.30875000 -1.13375000
92 -1.19625000 -1.30875000
93 -1.14625000 -1.19625000
94 -1.14625000 -1.14625000
95 -1.29625000 -1.14625000
96 NA -1.29625000
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.76625000 -0.56625000
[2,] -1.12875000 -0.76625000
[3,] -1.42875000 -1.12875000
[4,] -1.54125000 -1.42875000
[5,] -1.37875000 -1.54125000
[6,] -0.99125000 -1.37875000
[7,] -0.76625000 -0.99125000
[8,] -0.95375000 -0.76625000
[9,] -1.30375000 -0.95375000
[10,] -1.20375000 -1.30375000
[11,] -0.85375000 -1.20375000
[12,] -0.22416667 -0.85375000
[13,] -0.12416667 -0.22416667
[14,] -0.08666667 -0.12416667
[15,] -0.18666667 -0.08666667
[16,] -0.29916667 -0.18666667
[17,] -0.23666667 -0.29916667
[18,] -0.24916667 -0.23666667
[19,] -0.22416667 -0.24916667
[20,] -0.21166667 -0.22416667
[21,] -0.06166667 -0.21166667
[22,] -0.06166667 -0.06166667
[23,] 0.08833333 -0.06166667
[24,] 0.21791667 0.08833333
[25,] 0.41791667 0.21791667
[26,] 0.45541667 0.41791667
[27,] 0.45541667 0.45541667
[28,] 0.44291667 0.45541667
[29,] 0.40541667 0.44291667
[30,] 0.49291667 0.40541667
[31,] 0.41791667 0.49291667
[32,] 0.53041667 0.41791667
[33,] 0.58041667 0.53041667
[34,] 0.58041667 0.58041667
[35,] 0.63041667 0.58041667
[36,] 0.56000000 0.63041667
[37,] 0.66000000 0.56000000
[38,] 0.79750000 0.66000000
[39,] 0.99750000 0.79750000
[40,] 0.98500000 0.99750000
[41,] 0.64750000 0.98500000
[42,] 0.03500000 0.64750000
[43,] -0.04000000 0.03500000
[44,] 0.37250000 -0.04000000
[45,] 1.12250000 0.37250000
[46,] 1.52250000 1.12250000
[47,] 1.47250000 1.52250000
[48,] 0.76500000 1.47250000
[49,] 0.36500000 0.76500000
[50,] 0.40250000 0.36500000
[51,] 0.50250000 0.40250000
[52,] 0.69000000 0.50250000
[53,] 0.85250000 0.69000000
[54,] 0.64000000 0.85250000
[55,] 0.76500000 0.64000000
[56,] 0.77750000 0.76500000
[57,] 0.82750000 0.77750000
[58,] 0.82750000 0.82750000
[59,] 0.87750000 0.82750000
[60,] 0.50708333 0.87750000
[61,] 0.50708333 0.50708333
[62,] 0.54458333 0.50708333
[63,] 0.64458333 0.54458333
[64,] 0.73208333 0.64458333
[65,] 0.79458333 0.73208333
[66,] 0.88208333 0.79458333
[67,] 0.90708333 0.88208333
[68,] 0.81958333 0.90708333
[69,] 0.36958333 0.81958333
[70,] 0.16958333 0.36958333
[71,] 0.11958333 0.16958333
[72,] -0.15083333 0.11958333
[73,] -0.05083333 -0.15083333
[74,] -0.11333333 -0.05083333
[75,] -0.11333333 -0.11333333
[76,] -0.02583333 -0.11333333
[77,] 0.03666667 -0.02583333
[78,] 0.32416667 0.03666667
[79,] 0.24916667 0.32416667
[80,] -0.13833333 0.24916667
[81,] -0.38833333 -0.13833333
[82,] -0.68833333 -0.38833333
[83,] -1.03833333 -0.68833333
[84,] -1.10875000 -1.03833333
[85,] -1.00875000 -1.10875000
[86,] -0.87125000 -1.00875000
[87,] -0.87125000 -0.87125000
[88,] -0.98375000 -0.87125000
[89,] -1.12125000 -0.98375000
[90,] -1.13375000 -1.12125000
[91,] -1.30875000 -1.13375000
[92,] -1.19625000 -1.30875000
[93,] -1.14625000 -1.19625000
[94,] -1.14625000 -1.14625000
[95,] -1.29625000 -1.14625000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.76625000 -0.56625000
2 -1.12875000 -0.76625000
3 -1.42875000 -1.12875000
4 -1.54125000 -1.42875000
5 -1.37875000 -1.54125000
6 -0.99125000 -1.37875000
7 -0.76625000 -0.99125000
8 -0.95375000 -0.76625000
9 -1.30375000 -0.95375000
10 -1.20375000 -1.30375000
11 -0.85375000 -1.20375000
12 -0.22416667 -0.85375000
13 -0.12416667 -0.22416667
14 -0.08666667 -0.12416667
15 -0.18666667 -0.08666667
16 -0.29916667 -0.18666667
17 -0.23666667 -0.29916667
18 -0.24916667 -0.23666667
19 -0.22416667 -0.24916667
20 -0.21166667 -0.22416667
21 -0.06166667 -0.21166667
22 -0.06166667 -0.06166667
23 0.08833333 -0.06166667
24 0.21791667 0.08833333
25 0.41791667 0.21791667
26 0.45541667 0.41791667
27 0.45541667 0.45541667
28 0.44291667 0.45541667
29 0.40541667 0.44291667
30 0.49291667 0.40541667
31 0.41791667 0.49291667
32 0.53041667 0.41791667
33 0.58041667 0.53041667
34 0.58041667 0.58041667
35 0.63041667 0.58041667
36 0.56000000 0.63041667
37 0.66000000 0.56000000
38 0.79750000 0.66000000
39 0.99750000 0.79750000
40 0.98500000 0.99750000
41 0.64750000 0.98500000
42 0.03500000 0.64750000
43 -0.04000000 0.03500000
44 0.37250000 -0.04000000
45 1.12250000 0.37250000
46 1.52250000 1.12250000
47 1.47250000 1.52250000
48 0.76500000 1.47250000
49 0.36500000 0.76500000
50 0.40250000 0.36500000
51 0.50250000 0.40250000
52 0.69000000 0.50250000
53 0.85250000 0.69000000
54 0.64000000 0.85250000
55 0.76500000 0.64000000
56 0.77750000 0.76500000
57 0.82750000 0.77750000
58 0.82750000 0.82750000
59 0.87750000 0.82750000
60 0.50708333 0.87750000
61 0.50708333 0.50708333
62 0.54458333 0.50708333
63 0.64458333 0.54458333
64 0.73208333 0.64458333
65 0.79458333 0.73208333
66 0.88208333 0.79458333
67 0.90708333 0.88208333
68 0.81958333 0.90708333
69 0.36958333 0.81958333
70 0.16958333 0.36958333
71 0.11958333 0.16958333
72 -0.15083333 0.11958333
73 -0.05083333 -0.15083333
74 -0.11333333 -0.05083333
75 -0.11333333 -0.11333333
76 -0.02583333 -0.11333333
77 0.03666667 -0.02583333
78 0.32416667 0.03666667
79 0.24916667 0.32416667
80 -0.13833333 0.24916667
81 -0.38833333 -0.13833333
82 -0.68833333 -0.38833333
83 -1.03833333 -0.68833333
84 -1.10875000 -1.03833333
85 -1.00875000 -1.10875000
86 -0.87125000 -1.00875000
87 -0.87125000 -0.87125000
88 -0.98375000 -0.87125000
89 -1.12125000 -0.98375000
90 -1.13375000 -1.12125000
91 -1.30875000 -1.13375000
92 -1.19625000 -1.30875000
93 -1.14625000 -1.19625000
94 -1.14625000 -1.14625000
95 -1.29625000 -1.14625000
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7hx2j1227785842.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8jy8y1227785842.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9yx871227785842.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10dhce1227785843.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11x6xy1227785843.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12ompq1227785843.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13w6aa1227785843.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/147oi41227785843.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15d7s51227785843.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16xdab1227785843.tab")
+ }
>
> system("convert tmp/1mk6o1227785842.ps tmp/1mk6o1227785842.png")
> system("convert tmp/23vlm1227785842.ps tmp/23vlm1227785842.png")
> system("convert tmp/33umy1227785842.ps tmp/33umy1227785842.png")
> system("convert tmp/44dq91227785842.ps tmp/44dq91227785842.png")
> system("convert tmp/5l3gm1227785842.ps tmp/5l3gm1227785842.png")
> system("convert tmp/6gaec1227785842.ps tmp/6gaec1227785842.png")
> system("convert tmp/7hx2j1227785842.ps tmp/7hx2j1227785842.png")
> system("convert tmp/8jy8y1227785842.ps tmp/8jy8y1227785842.png")
> system("convert tmp/9yx871227785842.ps tmp/9yx871227785842.png")
> system("convert tmp/10dhce1227785843.ps tmp/10dhce1227785843.png")
>
>
> proc.time()
user system elapsed
2.922 1.590 3.446